Design and Optimization of an Integrated Visible Light Communication and Localization System Using Liquid Crystal Based-RIS Receivers
Bibliographic record
Abstract
Visible Light Communication (VLC) is emerging as a pivotal technology in next-generation wireless networks, leveraging the abundant unlicensed spectrum to achieve ultra-high data rates with minimal energy overhead. This paper presents the design and optimization of a novel integrated VLC and localization (VLCL) system enhanced by liquid crystal-based reconfigurable intelligent surfaces (LC-RISs). The proposed system architecture facilitates simultaneous communication, localization, and illumination within indoor environments by dynamically adjusting the refractive index of the LC-RIS under an electric field to precisely control light propagation and focus. We address two core optimization challenges: sum rate maximization and energy efficiency maximization, both of which are formulated as constrained optimization problems. While the sum rate problem is convex and solvable using standard optimization techniques, the energy efficiency problem is non-convex, necessitating the development of a low-complexity solution via fractional programming. Simulation results substantiate the efficacy of the LC-RIS in significantly enhancing both sum rate and energy efficiency, particularly in scenarios with dense number of users and access point configurations, thereby demonstrating the LC-RIS's potential to substantially improve the performance of VLCL systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".